Preference Elicitation and Reasoning While Smart Shifting of Home Appliances

AbstractA crucial part of the total electricity demand is energy consumption in the residential sector. In parallel to optimizing energy consumption within houses, user comfort is still an essential success criterion for automated solutions used within the house. Choosing the most comfortable appliance schedule is often a challenging task for the members of the house. To bring focus on this challenge, residential customer involvement is enhanced by a trend towards automation of appliances. This trend is reflected by pilot projects such as Linear which uses automated smart appliances at the demand side to attain more flexibility in the electricity system. Moreover, industrial interest from the Telecom, energy and household appliance sector to promote smart schedules for appliances is growing. To meet this trend, this paper describes new ways to model and reason with the user preferences when scheduling appliances in a household under dynamic pricing schemes given different user preferences. These methods have been proven to be efficient in eliciting and computing the user preferences to increase the user comfort in the house

Argumentation-Based Preference Modelling with Incomplete Information

Abstract. No intelligent decision support system functions even remotely without knowing the preferences of the user. A major problem is that the way average users think about and formulate their preferences does not match the utility-based quantitative frameworks currently used in decision support systems. For the average user qualitative models are a better fit. This paper presents an argumentationbased framework for the modelling of and automated reasoning about multi-issue preferences of a qualitative nature. The framework presents preferences according to the lexicographic ordering that is well-understood by humans. The main contribution of the paper is that it shows how to reason about preferences when only incomplete information is available. An adequate strategy is proposed that allows reasoning with incomplete information and it is shown how to incorporate this strategy into the argumentation-based framework for modelling preferences